面向DSP的超字并行指令分析和冗余优化算法

如今单指令多数据流(SIMD)技术在数字信号处理器（DSP）上得到了广泛的应用，现有的向量化编译器大多都实现了自动向量化的功能，但是编译器并不适合支持DSP为特征的SIMD自动向量化，主要由于DSP复杂的指令集、特有的寻址模型，以及依赖关系或者数据非对齐等原因而导致向量化效率不高。为了解决此问题，在基于Open64的超字并行(SLP)自动向量化编译系统后端，对SLP自动向量化中的指令分析和冗余优化算法进行了添加和改进，生成更加高效的向量化源程序。实验结果表明，该优化方法能有效提高DSP性能并降低功耗。

Superword level parallelism instruction analysis and redundancy optimization algorithm on DSP

Today, SIMD (Single Instruction Multiple Data) technology has been widely used in Digital Signal Processor (DSP), and most of the existing compilers realize automatic vectorization functions. However，the compiler cannot support SIMD auto-vectorization with the feature of DSP, because of DSP complex instruction set, the specific addressing model, the obstacle of dependence relation to vectorization non-aligned data or other reasons. In order to solve this problem, in this paper, for the automatic vectorization in the Superword Level Parallelism (SLP) based on the Open64 compiler back end, the instruction analysis and redundancy optimization algorithm were improved, so as to transform more efficient vectorized source program. The experimental results show that the proposed method can improve DSP performances and reduce power consumption efficiently.